


from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


UpperCamelCase					:       Tuple			    =     logging.get_logger(__name__)



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		['input_features', 'attention_mask']

  def __init__( self			:  str				,				lowerCamelCase__			:  List[str]=80				,				lowerCamelCase__			:  Any=16_000				,				lowerCamelCase__			:  int=80				,				lowerCamelCase__			:  Optional[Any]=0.0				,				lowerCamelCase__			:  Union[str, Any]=True				,				lowerCamelCase__			:  Dict=True				,				lowerCamelCase__			:  Tuple=True				,				**lowerCamelCase__			:  Optional[int]				,				):
        super().__init__(feature_size=lowerCamelCase__				,				sampling_rate=lowerCamelCase__				,				padding_value=lowerCamelCase__				,				**lowerCamelCase__       )
        a__	:  Dict     =   num_mel_bins
        a__	:  Optional[int]     =   do_ceptral_normalize
        a__	:  List[str]     =   normalize_means
        a__	:  Any     =   normalize_vars
        a__	:  Optional[Any]     =   True

  def    _UpperCamelCase( self			:  Optional[Any]				,				lowerCamelCase__			:  np.ndarray				,				):
        a__	:  Tuple     =   waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
        a__	:  Union[str, Any]     =   torch.from_numpy(lowerCamelCase__       ).unsqueeze(0       )
        a__	:  str     =   ta_kaldi.fbank(lowerCamelCase__				,				num_mel_bins=self.num_mel_bins				,				sample_frequency=self.sampling_rate       )
        return features.numpy()

  @staticmethod
  def    _UpperCamelCase( lowerCamelCase__			:  np.ndarray				,				lowerCamelCase__			:  int				,				lowerCamelCase__			:  Optional[bool] = True				,				lowerCamelCase__			:  Optional[bool] = True				,				lowerCamelCase__			:  float = 0.0				,				):
        # make sure we normalize float32 arrays
        if normalize_means:
              a__	:  List[str]     =   x[:input_length].mean(axis=0       )
              a__	:  Dict     =   np.subtract(lowerCamelCase__				,				lowerCamelCase__       )
        if normalize_vars:
              a__	:  Any     =   x[:input_length].std(axis=0       )
              a__	:  List[str]     =   np.divide(lowerCamelCase__				,				lowerCamelCase__       )

        if input_length < x.shape[0]:
              a__	:  str     =   padding_value

        # make sure array is in float32
        a__	:  List[Any]     =   x.astype(np.floataa       )

        return x

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  List[np.ndarray]				,				lowerCamelCase__			:  Optional[np.ndarray] = None       ):
        a__	:  List[str]     =   attention_mask.sum(-1       ) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [
            self.utterance_cmvn(lowerCamelCase__				,				lowerCamelCase__				,				self.normalize_means				,				self.normalize_vars				,				self.padding_value       )
            for x, n in zip(lowerCamelCase__				,				lowerCamelCase__       )
        ]

  def __call__( self			:  str				,				lowerCamelCase__			:  Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,				lowerCamelCase__			:  Union[bool, str, PaddingStrategy] = False				,				lowerCamelCase__			:  Optional[int] = None				,				lowerCamelCase__			:  bool = False				,				lowerCamelCase__			:  Optional[int] = None				,				lowerCamelCase__			:  Optional[Union[str, TensorType]] = None				,				lowerCamelCase__			:  Optional[int] = None				,				lowerCamelCase__			:  Optional[bool] = None				,				**lowerCamelCase__			:  List[Any]				,				):

        if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                        f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
                        f''' {self.sampling_rate} and not {sampling_rate}.'''       )
        else:
              logger.warning(
                  "It is strongly recommended to pass the `sampling_rate` argument to this function. "
                  "Failing to do so can result in silent errors that might be hard to debug."       )

        a__	:  List[str]     =   isinstance(lowerCamelCase__				,				np.ndarray       ) and len(raw_speech.shape       ) > 1
        if is_batched_numpy and len(raw_speech.shape       ) > 2:
              raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''       )
        a__	:  Dict     =   is_batched_numpy or (
            isinstance(lowerCamelCase__				,				(list, tuple)       ) and (isinstance(raw_speech[0]				,				(np.ndarray, tuple, list)       ))
        )

        if is_batched:
              a__	:  str     =   [np.asarray(lowerCamelCase__				,				dtype=np.floataa       ) for speech in raw_speech]
        elif not is_batched and not isinstance(lowerCamelCase__				,				np.ndarray       ):
              a__	:  Tuple     =   np.asarray(lowerCamelCase__				,				dtype=np.floataa       )
        elif isinstance(lowerCamelCase__				,				np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
              a__	:  str     =   raw_speech.astype(np.floataa       )

        # always return batch
        if not is_batched:
              a__	:  List[str]     =   [raw_speech]

        # extract fbank features
        a__	:  Any     =   [self._extract_fbank_features(lowerCamelCase__       ) for waveform in raw_speech]

        # convert into correct format for padding
        a__	:  Any     =   BatchFeature({"input_features": features}       )

        a__	:  Any     =   self.pad(
            lowerCamelCase__				,				padding=lowerCamelCase__				,				max_length=lowerCamelCase__				,				truncation=lowerCamelCase__				,				pad_to_multiple_of=lowerCamelCase__				,				return_attention_mask=lowerCamelCase__				,				**lowerCamelCase__				,				)

        # make sure list is in array format
        a__	:  List[str]     =   padded_inputs.get("input_features"       )
        if isinstance(input_features[0]				,				lowerCamelCase__       ):
              a__	:  Optional[int]     =   [np.asarray(lowerCamelCase__				,				dtype=np.floataa       ) for feature in input_features]

        a__	:  Dict     =   padded_inputs.get("attention_mask"       )
        if attention_mask is not None:
              a__	:  Optional[int]     =   [np.asarray(lowerCamelCase__				,				dtype=np.intaa       ) for array in attention_mask]

        # Utterance-level cepstral mean and variance normalization
        if self.do_ceptral_normalize:
              a__	:  Optional[Any]     =   (
                  np.array(lowerCamelCase__				,				dtype=np.intaa       )
                  if self._get_padding_strategies(lowerCamelCase__				,				max_length=lowerCamelCase__       ) is not PaddingStrategy.DO_NOT_PAD
                  else None
              )
              a__	:  Optional[Any]     =   self.normalize(
                  padded_inputs["input_features"]				,				attention_mask=lowerCamelCase__       )

        if return_tensors is not None:
              a__	:  Optional[Any]     =   padded_inputs.convert_to_tensors(lowerCamelCase__       )

        return padded_inputs



"""simple docstring"""



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


a       :						int       		=		5_0_0_0_0_0

a		,			a       :						Union[str, Any]       		=		os.path.split(__file__)
a       :						Dict       		=		os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Any:





    """simple docstring"""

    _UpperCAmelCase        = dataset.map(**_A   )


@get_duration
def 			_UpperCamelCase						(    _A       ,   **_A   )     ->	Dict:





    """simple docstring"""

    _UpperCAmelCase        = dataset.filter(**_A   )


def 			_UpperCamelCase						(    )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = {"""num examples""": SPEED_TEST_N_EXAMPLES}
    with tempfile.TemporaryDirectory() as tmp_dir:
        _UpperCAmelCase        = datasets.Features({"""text""": datasets.Value("""string"""   ), """numbers""": datasets.Value("""float32"""   )}   )
        _UpperCAmelCase        = generate_example_dataset(
            os.path.join(_A       ,   """dataset.arrow"""   )       ,   _A       ,   num_examples=_A   )

        _UpperCAmelCase        = transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""       ,   use_fast=_A   )

        def tokenize(_A   ):
            return tokenizer(examples["""text"""]   )

        _UpperCAmelCase        = map(_A   )

        _UpperCAmelCase        = map(_A       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""numpy"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""pandas"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""torch"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        with dataset.formatted_as(type="""tensorflow"""       ,   columns="""numbers"""   ):
            _UpperCAmelCase        = map(_A       ,   function=lambda _A   : None       ,   batched=_A   )

        _UpperCAmelCase        = map(_A       ,   function=_A       ,   batched=_A   )

        _UpperCAmelCase        = filter(_A   )

        # Activate later when tokenizer support batched inputs
        # with dataset.formatted_as(type='numpy'):
        #     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

    with open(_A       ,   """wb"""   ) as f:
        f.write(json.dumps(_A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_map_filter()